Morph: Flexible Acceleration for 3D CNN-based Video Understanding

Abstract--The past several years have seen both an explosion in the use of Convolutional Neural Networks (CNNs) and the design of accelerators to make CNN inference practical. In the architecture community, the lion share of effort has targeted CNN inference for image recognition. The closely related problem of video recognition has received far less attention as an accelerator target. This is surprising, as video recognition is more computationally intensive than image recognition, and video traffic is predicted to be the majority of internet traffic in the coming years. This paper fills the gap between algorithmic and hardware advances for video recognition by providing a design space exploration and flexible architecture for accelerating 3D Convolutional Neural Networks (3D CNNs)--the core kernel in modern video understanding. When compared to (2D) CNNs used for image recognition, efficiently accelerating 3D CNNs poses a significant engineering challenge due to their large (and variable over time) memory footprint and higher dimensionality. To address these challenges, we design a novel accelerator, called Morph, that can adaptively support different spatial and temporal tiling strategies depending on the needs of each layer of each target 3D CNN. We codesign a software infrastructure alongside the Morph hardware to find good-fit parameters to control the hardware. Evaluated on state-of-the-art 3D CNNs, Morph achieves up to 3.4 (2.5 average) reduction in energy consumption and improves performance/watt by up to 5.1 (4 average) compared to a baseline 3D CNN accelerator, with an area overhead of 5%. Morph further achieves a 15.9 average energy reduction on 3D CNNs when compared to Eyeriss. The rise of Convolutional Neural Networks (CNNs) [1], [2], [3], [4] has marked tremendous progress in image recognition, advancing the state-of-the-art in tasks ranging from handwritten digit [5] to complex object recognition [6], [7]. At their core, CNNs are compute intensive, parallel dot product operations. Combined with their importance, this computation style has made CNNs a natural target for hardware ASIC acceleration, and a rich line of work has made large strides in this direction [8], [9], [10], [11], [12], [13]. Given the recent progress in image recognition, a natural question is whether similar strides have been made for the related problem of video recognition. This work was partially supported by NSF award CCF-1725734 and a DARPA SDH contract. Authors contributed equally to this work. Current state-of-the-art results are achieved using 3-dimensional (3D) CNNs, which generalize (2D) CNNs used for image recognition to account for the time dimension, thereby allowing the model to capture spatiotemporal features.